Brain Network Mechanisms of Instructed Learning

指导学习的脑网络机制

• 批准号: 9235846
• 负责人: Michael William Cole
• 金额: $ 41.19万
• 依托单位: RUTGERS THE STATE UNIV OF NJ NEWARK
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-13 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9235846
• 关键词: Accounting; Auditory; Behavior; Brain; Brain region; Clinic; Cognition; Cognitive; Cognitive Therapy; Disease; Drops; Effectiveness; Feedback; Fostering; Goals; Habits; Human; Individual; Instruction; Language; Lead; Learning; Life; Link; Literature; Major Depressive Disorder; Maps; Mediating; Mental Health; Mental disorders; Methods; Motor; Network-based; Neurosciences; Pathway Analysis; Pattern; Performance; Play; Post-Traumatic Stress Disorders; Process; Psyche structure; Psychotherapy; Research; Rest; Role; Schizophrenia; Science; Sensory; Specificity; System; Testing; Time; Training; Update; Work; abstracting; base; behavior change; classical conditioning; cognitive control; cognitive function; cognitive neuroscience; cognitive training; flexibility; improved; improved outcome; injured; insight; neuroimaging; novel; relating to nervous system; skills; theories; tool; visual motor

Project Summary/Abstract: The tools of network science have enabled substantial progress in understanding the intrinsic organization of the human brain. Yet, the predominant focus on resting-state functional connectivity (FC) has become a critical barrier to progress in cognitive neuroscience, given that rest FC does not account for task-specific network changes likely essential for adaptive cognition. We offer a complementary approach – cognitive network neuroscience – which applies dynamic network analysis tools and theories to task manipulations of FC to offer insights into human cognitive function. The goal of this proposal is to utilize this network-based approach with human neuroimaging to understand how instructed learning is implemented in the human brain, from initial learning to automaticity after extensive practice. Most neuroscientific research has focused on non-instructed (e.g., exploratory or feedback-based) learning. Yet, instructed learning is highly relevant to mental health for several reasons. First, empirically supported psychotherapies (e.g., cognitive behavioral therapy) utilize the human ability for rapid instructed task learning (RITL; “rittle”) to convert instructions into cognitive strategies that improve outcomes across nearly every major mental disease. Second, RITL is impaired in a variety of mental diseases, given that RITL relies on flexible cognitive control – a general capacity supporting adaptive, goal-directed behavior important in daily life. Thus, in addition to adding difficulties to everyday life (e.g., learning new skills at work), the disruption of RITL abilities likely limits the effectiveness of psychotherapy in improving mental health. Finally, instructed learning provides an especially powerful means of experimental control over behavior change, which underlies mental health improvements even outside the context of psychotherapy. Advancing understanding of the neural basis of RITL and its transition to practiced automatized behaviors parallels the transition from instructions in the clinic to ingrained habits that can foster successful mental health change. In prior work, we built a large-scale brain network theory for how instructed learning occurs by drawing on the concept of “flexible hubs” – brain regions that coordinate goal-directed cognition (flexible control) by dynamically updating connectivity throughout the brain. The flexible hub theory strongly links the methods and theories of network science to the cognitive neuroscience of learning, and as such has the power to offer insights into the large-scale network processes underlying instructed learning. We propose to use large-scale brain network theory to understand the domain generality of flexible hubs during instructed learning (Aim 1), to determine the role of flexible hubs in the transition from novel instructed task training to practiced performance (Aims 2.1 & 2.2), and to develop RITL cognitive training that maximizes the utility of flexible hubs for performance of novel tasks (Aim 2.3). Our network-based approach to understanding instructed learning along with RITL cognitive training may lead to improved outcomes for a variety of mental disorders, given the central role instructed learning plays in empirically supported psychotherapies.

项目概要/摘要：网络科学的工具使我们在理解人类大脑的内在组织方面取得了实质性进展。然而，休息状态功能连接（FC）的主要重点已成为认知神经科学进步的关键障碍，因为休息FC不占任务特定的网络变化可能是必要的适应性认知。我们提供了一个互补的方法-认知网络神经科学-应用动态网络分析工具和理论的任务操作的FC提供洞察人类的认知功能。该提案的目标是利用这种基于网络的方法与人类神经成像，以了解指令学习是如何在人脑中实现的，从最初的学习到广泛的实践后的自动化。大多数神经科学研究都集中在非指导（例如，探索性或基于反馈的）学习。然而，由于几个原因，指导学习与心理健康高度相关。首先，经验支持的心理治疗（例如，认知行为疗法（cognitive behavioral therapy）利用人类快速指令任务学习（RITL;“rittle”）的能力，将指令转化为认知策略，从而改善几乎所有主要精神疾病的结果。第二，RITL在各种精神疾病中受损，因为RITL依赖于灵活的认知控制-一种在日常生活中支持适应性，目标导向行为的一般能力。因此，除了给日常生活增加困难之外（例如，在工作中学习新技能），RITL能力的中断可能会限制心理治疗在改善心理健康方面的有效性。最后，指导学习提供了一种特别强大的实验控制行为变化的手段，即使在心理治疗的背景下，这也是心理健康改善的基础。推进对RITL神经基础的理解及其向实践自动化行为的过渡，与从临床指导向可以促进成功的心理健康改变的根深蒂固的习惯的过渡是平行的。在之前的工作中，我们建立了一个大规模的大脑网络理论，用于指导学习如何通过“灵活中枢”的概念来进行-通过动态更新整个大脑的连接来协调目标导向认知（灵活控制）的大脑区域。柔性枢纽理论将网络科学的方法和理论与学习的认知神经科学紧密联系起来，因此有能力深入了解指导学习背后的大规模网络过程。我们建议使用大规模脑网络理论来理解灵活枢纽在指导学习过程中的域一般性（目标1），确定灵活枢纽在从新的指导任务训练到实践表现的过渡中的作用（目标2.1和2.2），并开发RITL认知训练，最大限度地提高灵活枢纽在新任务表现中的效用（目标2.3）。我们基于网络的方法来理解指示学习沿着RITL认知训练可能会导致各种精神障碍的改善结果，因为指示学习在经验支持的心理治疗中发挥着核心作用。